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Article

Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data

by
Hanan Kaduim Mohammed
1,*,
Imzahim A. Alwan
1 and
Mahmoud Saleh Al-Khafaji
2
1
Civil Engineering Department, University of Technology-Iraq, Alsinaa Street 52, Baghdad 10066, Iraq
2
Department of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad 10071, Iraq
*
Author to whom correspondence should be addressed.
Hydrology 2025, 12(4), 69; https://doi.org/10.3390/hydrology12040069
Submission received: 20 January 2025 / Revised: 1 March 2025 / Accepted: 7 March 2025 / Published: 26 March 2025
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Abstract

Iraq and other semi-arid regions are facing severe climate change impacts, including increased temperatures and decreased rainfall. Changes to climate variables have posed a significant challenge to groundwater storage dynamics. In this regard, the Gravity Recovery and Climate Experiment (GRACE) mission permits novel originate groundwater storage variations. This study used the monthly GRACE satellite data for 2002–2023 to determine variations in groundwater storage (GWS). Changes in GWS were implied by extracting soil moisture, acquired from the Global Land Data Assimilation System (GLDAS), from the extracted Territorial Water Storage (TWS). The results demonstrated that an annual average ΔGWS trend ranged for the Goddard Space Flight Center (GSFC) mascon and Jet Propulsion Laboratory (JPL) mascon was from 0.94 to −1.14 cm/yr and 1.64 to −1.36 cm/yr, respectively. Also, the GSFC illustrated superior performance in estimating ΔGWS compared with the JPL in Iraq, achieving the lowest root mean square error at 0.28 mm and 0.60 mm and the highest coefficient of determination (R2) at 0.92 and 0.89, respectively. These data are critical for identifying areas of depletion, especially in areas where in situ data are lacking. These data allows us to fill the knowledge gaps; provide critical scientific information for monitoring and managing dynamic variations.

1. Introduction

The problem of water scarcity is severe worldwide and the World Economic Forum has included the issue among the top challenges facing humanity [1]. The Middle East, predominantly arid and semi-arid regions, has significant water challenges in terms of a severe shortage of water resources [2,3]. Water scarcity, particularly in arid and semi-arid areas like Iraq, provides substantial challenges [4], where a lack of water is an issue for domestic and agricultural use [5]. The Middle East is one of the most impacted regions in the world regarding climate change [6]. The changing climate significantly impacts hydrological dynamics [7]. On the other hand, evaporation and precipitation directly affect water supply by reducing available water resources. Decreased precipitation and increased evaporation lead to decreased water availability, which may subsequently lead to increased water demand [3,8]. This situation has become increasingly severe in recent years due to repeated drought and population increase [9]. The rapid growth of populations has led to a significant surge in economic activity, including industrial expansion, urbanization, and the extension of agricultural areas. This increase, coupled with the intensification of severe weather conditions such as droughts and heat waves—particularly in arid and semi-arid regions where water shortages impact both domestic and agricultural needs—has exacerbated water scarcity [10,11,12]. Nowadays, the availability of water resources is crucial for ensuring a safe living environment [13]; some cities heavily rely on groundwater. Consequently, the rate at which groundwater is being used up cannot be kept with its replenishment [14]. This phenomenon is identified as groundwater depletion (GWD), causing a persistent decrease in groundwater levels [15]. The absence of a representation of values for aquifer storage parameters, which includes specific yield and storability, insufficient presence of groundwater observation wells, and inconsistent and irregular levels of groundwater observational information from the boundaries of current networks, make it challenging to comprehend fluctuations in groundwater storage changes in the area [16]. Traditional in situ monitoring methods are insufficient for monitoring accurate variations in water stocks on both spatial and temporal scales [17]. The aquifer’s depth, thickness, and permeability are essential in determining the optimum well site [18] and the position’s terrain [19]. Remote sensing (RS) is an effective means of gathering information about an item by evaluating data acquired from that item from a distance [20,21], and also an effective way of monitoring water storage dynamics at a global scale [22].
Starting from the time it was launched in 2002, the Gravity and Climate Experiment (GRACE) mission has played a crucial part in estimating changes in Earth’s water reserves by measuring changes in the gravitational field of the plant [23,24]. Because of its greater coverage, ease of use, and connection with other data sources, this approach is more beneficial than others [25]. The results for groundwater storage were validated utilizing wells observation data; the terrestrial water storage (TWS) fluctuation data were primarily derived from Darcy’s law, water balance, and GRACE across the basin’s boundaries. The root mean square error (RMSE), R-square, Mann–Kendall (M-K) test, and Sen’s slope were utilized to study the altering trend as a validity indicator. By using a combined groundwater and surface water model noted as the MODFLOW, the One-Water Hydrologic Flow Model (MF-OWHM) was able to model groundwater to determine water budgets [26]. Groundwater modeling is critical for understanding the mechanics of groundwater flow and the interplay with different hydrogeological factors [27,28,29]. Ref. [30] utilized multi-layer perceptrons (MLPs) in Telangana state, India, for the period from 2003 to 2020. GRACE data were integrated with the GLDAS results to obtain GWS. Afterward, it was transformed into seasonal values and contrasted with GWS values derived from observational well data (GWS_obs). The study revealed a strong correlation between GWS derived from GRACE and GWS based on observation, with a correlation coefficient (R) of 0.74 and Nash Sutcliffe Efficiency (NSE) of 0.60 [31]. Ref. [32] employed GRACE satellite data, climatic factor results, and precipitation information to investigate the long-term effects of groundwater-level fluctuations on the mass of various Iraqi areas. This study verified that Iraq’s groundwater supplies have generally decreased, which is primarily caused by activity from humans and a decline in precipitations, especially after the drought started, in 2007.
GRACE satellite data and land surface model outputs were utilized to evaluate TWS variations in the north–central Middle East (the Tigris–Euphrates River Basin and northern Iran). Based on their results, the rate of reduction in TWS was −2.72 ± 0.06 cm/yr. Thus, the region declined by 1.73 ± 0.21 cm/yr of equivalent water height of groundwater (GW) from 2002 to 2009 [33]. The newest gravity field data obtained by gravitational simulations of the Earth were utilized for analyzing the elements and properties of the crust at the major scale [34]. Groundwater study, underground geology [35], and land subsidence [36] all employed smaller-scale geophysics information collected from aircraft as well as land. The authors of [32] utilized GRACE data and GLDAS outcomes, in addition to data on rainfall, in evaluating the GW mass fluctuations of space and time. The average ΔGW were −4.86 ± 0.68, +2.47 ± 2.20, and −3.79 ± 1.20 mm/yr for the periods from 2002 to 2006, 2007 to 2017, and 2018 to 2020, respectively. Meanwhile, the average ΔTWS were +6.82 ± 1.92, −6.20 ± 1.17, and +28.58 ± 12.78 mm/yr for the periods from 2002 to 2006, 2007 to 2017, and 2018 to 2020, respectively. Overall, this study revealed that the TWS is negative in northern Iraq and positive in southern Iraq. During the study period, the GWS revealed a total depletion rate of −4.63 ± 12.99 mm/yr over Iraq. In a similar context [37], utilizing GRACE data and data on 517 observed wells, GW levels were estimated with a RMSE at 53 cm and a R2 at 0.996 when compared to observed data to evaluate GW depletion throughout the study period.
This study aims to analyze short-term groundwater changes in Iraq utilizing GRACE and CLADAS data. This paper seeks to fill the knowledge gap associated with understanding the temporal and spatial dynamics of groundwater variations, identifying depletion rates and the most affected regions. It also aims to provide new scientific insights to support water resources management strategies in light of the challenges associated with data shortages and climate change.

2. Materials and Methods

2.1. Study Area

Iraq is located in southwest Asia at a latitude of 29° 15′–38° 15′ N and longitude of 38° 45′–48° 45′ E, covering an area of 437,072 km2, where water bodies form 4910 km2 and landforms 432,162 km2 of the total area [38]. Iraq is experiencing severe drought and a growing decline in zones covered by greenery due to decreased precipitation during the winter and increased temperatures. Furthermore, it mostly depends on surface water from nearby nations [39]. Because Iraq is a downstream nation, it is in a crucial location because it suffers from being situated upstream of the Tigris and Euphrates rivers [40]. Iraq is experiencing a scarcity of water resources [41]. Its climate is fundamentally continental, subtropical, and semi-arid [42]. The annual rainfall fluctuates between 120 cm in the north–east and lower than 10 cm, representing more than 60% of the country area in the south, while the total mean precipitation is 21.6 cm. Winters are chilly to frigid, with temperatures throughout the day around 16 °C and night-time temperatures decreasing to as little as 2 °C. Summertime is hot and arid; the shading temperature is above 43 °C through July and August, decreasing from midnight to 26 °C [43]. Iraq has been hydrogeologically classified into seven distinct regions based on physiographical, structural, geological, and hydrogeological characteristics [44]. The primary source of water in Iraq is surface water. With their tributaries, the Tigris and Euphrates rivers represent the backbone of Iraq’s water resources (86%). Meanwhile, the remaining percentage of resources for water (14%) originates from shallow and deep groundwater aquifers [45]. Iraq experienced an alarming increase in groundwater consumption, rising from 4.03 billion cubic meters (BCM) in 2015 to 4.30 BCM in 2020. Agricultural production represents the most significant contributor to this demand, increasing from 3.22 BCM in 2015 to 3.39 BCM in 2020. Expectations indicate that groundwater consumption in Iraq will significantly increase in the coming decades, as it is predicted to rise from 2.81 BCM in 2015 to 4.63 BCM in 2035, a 65% increase over current levels [46].

2.2. Data Sources and Data Assembling

One of the vital natural resources is groundwater, dispersed randomly throughout time and location. Throughout dry times, groundwater can also be used as a natural storage alternative to surface reservoirs [47]. In many areas, obtaining field data on water storage variations spatially and temporally is difficult because of the expensive or limited observational networks. Satellite data have become a useful tool for Earth observation in recent years. As a technique, remote sensing distinctly differs from more ‘traditional’ approaches to investigating and understanding hydrological processes. Studies have established that satellite observation is efficient and reliable for water resource assessment [48,49]. Thus, hydrological models have been enhanced to achieve extensive coverage, adequate time continuity, and data accuracy. By incorporating all of the water balance equation’s components, like precipitation, evaporation, snow and ice, soil moisture (SM), river discharge, GWS variations, and other extra components that could be involved in or be impacted by hydrological variations, such as land cover and land use through active or passive sensors or microgravity sensors [50]. Hitherto, satellite data analysis has become much more feasible due to the advancement of satellite sensing technology and data analysis algorithms on surface and groundwater resources. The time-varying gravity field has been measured by the GRACE mission that was launched in the year 2002. From these data, systematic changes in land water storage change (LWSC) or TWS as a function of space and time on monthly or sub-monthly timescales can be deduced [51]. LWSC consists of snow or glacier variations, surface water, soil moisture, and groundwater. Thus, LWSC can be separated from GWSC by combining GRACE information with external data [52], such as in situ information, hydrological simulations, and remote sensing information [53]. The findings of land surface models or hydrological models, like those from GLDAS, are utilized [54]. One important instrument for GWSC detection at the global level is the GRACE mission [37,55] and also at regional levels [33,56].

2.2.1. GRACE

GRACE involves two satellites that follow each other at a distance of roughly 220 km in a polar orbit at an altitude of around 500 km [57]. Launched in March 2002 by NASA and the German Aerospace Center (DLR) [46,58] to measure changes in the Earth’s gravitational field and utilized their data to investigate changes in water resources throughout land, ice, and oceans [51]. The GRACE mission concluded in November 2017 because of age-related problems with the batteries of GRACE [59]. The GRACE Follow On (GRACE-FO) mission was launched in May 2018 [60]. Anomalies in gravity caused by the variations in mass inside water bodies, involving soil moisture, surface water, snow, and groundwater, were measured. Since changes in the Earth’s gravity field mirror mass variations in the surface, these gravity field measurements (obtained monthly) can be utilized to track variations in TWS over large basins, exceeding the limitations of the GRACE spatial resolution [61]. As water in an aquifer declines, it decreases the gravitational force at the site [62]. GRACE satellite data are made at a 3° × 3° (lat. and long.) resolution, yet are also rebuilt and published at a 0.5° × 0.5° spatial resolution [63], where 1° × 1° is equivalent to the sum area (111 × 111) km [64].

2.2.2. Global Land Data Assimilation System (GLDAS)

GLDAS is a collaboration between the National Aeronautics Space Administration (NASA), the National Center for Forecasting the Environment, and the National Oceanic and Atmospheric Administration (NOAA) [65]. The GLDAS models the flux association between the surface and the atmosphere using the land surface model, which uses hydrological data from satellites and ground and presents assimilation data for the results [54]. The GLDAS includes hydrological data utilizing four LSMs: the Community Land Model (CLM), Noah, Variable Infiltration Capacity (VIC), and Mosaic Model [66,67]. Nevertheless, since there are currently no accurate and spatially continuous measurements of soil moisture, soil moisture storage (SMS), surface water storage (SWS), canopy water storage (CWS), and snow water equivalent (SWE) estimates are derived from outputs of advanced land surface models supplied by GLDAS [54]. Since the NOAH model simulates variations in water storage with less bias and uncertainty than other GLDAS models, it was chosen for this investigation [68].
A variety of data sources, including land surface models (LSMs), GRACE monthly TWS, and extra data, such as meteorological data, are employed in this paper. The data types and sources utilized for the study covering the period of April 2002 to December 2023 are described in Table 1. The signal is converted into a mass variation signal to learn more about groundwater dynamics after obtaining gravimetric data from GRACE, which can be directly associated with water source variations [69,70]. Datasets representing hydrologic elements are integrated with GRACE’s TWS for calculating groundwater. There are two types of GRACE solutions available: mass concentrations (Mas-cons) and conventional spherical harmonics (SH) [71]. The mascon solutions are ready-to-use maps of equivalent water elevations, which can be used directly without post-processing. The benefit of mascon data lies in its distinction between land and water.
On the other hand, the SH form has more significant uncertainty due to the need to apply the ocean and leakage effect correction and restore signals [72]. GRACE mascon provided the monthly TWS for Iraq; the mascon products are represented by JPL, GSFC, and CSR datasets from the Jet Propulsion Laboratory (JPL), the NASA Goddard Space Flight Center (GSFC), and the University of Texas Center for Space Research (CSR), respectively, and 210 monthly usages per mascon. GLDAS’s monthly releases were utilized to make combining the GRACE data easier. At the same time, GLDAS model NOAH025_Mv2.1 was utilized to determine the monthly SMS, SWE, SWS, and CWS. The GLDAS outcomes are measured in kilograms per square meter. Since GRACE data outputs are in centimeters, the units of the GLDAS data have to be brought to the same scale to match. This is because all the GLDAS findings are in kg per m³ and divided by water density (1000 kg/m³); units are in meters, and, therefore, all metric values are converted to centimeters.

2.3. Methods

The requirement to assess the dynamics of spatiotemporal changes in groundwater storage is enforced in light of the rising scarcity of water. Due to climate change, Iraq is witnessing a significant increase in water demand as a result of increasing temperatures and decreasing rainfall rates, leading to the rapid depletion of GWS. By analyzing a variety of data, researchers seek to understand the spatial and temporal variations in GWS, including the gravity data, hydrological models, rainfall, drought intensity, and in situ well data. The focus of this study is to analyze the temporal and spatial trends of groundwater levels in Iraq utilizing satellite data from 2002 to 2023 and the extent to which precipitation rate fluctuations influence this change. Multiple statistical analytical methods are employed to evaluate change. Figure 1 summarizes the main framework of the methodology utilized in this study.

2.3.1. Computation of GRACE-Derived ΔGWS

GLDAS’s terrestrial hydroclimatology can be used to investigate variations in water storage. However, TWS involves all surface and underground water (e.g., surface and groundwater). Several scientists utilized Equation (1) to compute the change in TWS, e.g., [73]. The trend in ΔGWS was calculated from the ΔTWS by subtracting other derived ΔTWS from GLDAS data.
∆ T W S = ∆ S M S + ∆ C W S + ∆ S W S + ∆ S W E + ∆ G W S
where ΔTWS, ΔSMS, ΔCWS, ΔSWS, ΔSWE, and ΔGWS refer to the changes in the terrestrial water storage, soil moisture storage, canopy water storage, surface water storage, snow water equivalent, and groundwater storage, respectively. The change (Δ) in the two values was applied by subtracting the new year’s values from the old year’s values for all years of the study period, 2002–2023.
The SMS was calculated for each vertical layer, 0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm [52]. This was performed by using the point Sum Raster Calculator of the SMS dataset. It has been utilized to generate a single raster image for the SMS of each year so that all four parameters are totaled up for each year. Then, the SMS is gained utilizing Equation (2).
S M S = [ S M 0 − 10 cm + S M 10 − 40 cm + S M 40 − 100 cm + S M 100 − 200 cm ]

2.3.2. Statistical Trend Analysis

To evaluate the results, i.e., the soundness of models used for computing the ΔGWS within this paper, the following statistical parameters were analyzed and determined a rising positive value trend: Sen’s slope and Mann–Kendall tests. Inversely, a declining trend is a negative value [74,75,76]. It has been frequently employed in water resources and climate research [77,78]. Also, the RMSE, NSE, R, and Percent Bias (PBIAS) were utilized to determine model validity.
The NSE frequently measures model accuracy in hydrological investigations, ranging from –∞ to 1. NSE is without units; one value indicates that the observed values were perfectly simulated, and negative numbers indicate bad simulation results [79]. The method seems satisfactory if it succeeds in attaining an RMSE value near zero [80]. On the other hand, the R is computed as the cross-correlation between the anticipated and observed values and ranges from 1 to −1. A value of −1 indicates that the values are perfectly negatively correlated, while a value of 1 indicates that the observed and simulated values are completely positively linked [81].
Finally, the optimal value for PBIAS is 0, and low magnitude values show accurate model simulation. Negative numbers show model overestimation bias and positive numbers show a model underestimate bias [82]. Based on earlier studies [83,84], a method seems satisfactory if it succeeds in NSE and R2 values above 0.50 and 0.65, respectively, while saving PBIAS below 25%. In addition, R values above 0.50 seem satisfactory [85].

3. Results and Discussion

The principal components of GRACE-TWS (SM, CWS, SWE, SWS, and GW) were used to estimate GWS, after applying Equations (1) and (2); elements such as CWS, SWE, and SWS did not show significant effects, as their mean values were close to zero. In semi-arid areas where gravimetric changes in SWS are negligible, contributions from ΔSWS are considered unimportant [86,87]. Therefore, it was ignored. In this regard, it may be concluded that these variables do not significantly contribute to the ΔTWS within the study area.

3.1. Computation of ΔGWS

Figure 2 and Figure 3 illustrate the ΔGWS over the study region from 2002 to 2023; the ΔGWS was estimated using GSFC and JPL from GRACE satellite data because the spatial resolution has the same 0.5 degree. Based on remote sensing data, estimated groundwater depletion rates in the northern region varied from 2004 to 2016. According to ΔGWS_GSFC data, the lowest consumption was −1.68 cm/yr, and the largest was −8.33 cm/yr. In contrast, ΔGWS_JPL data showed the lowest depletion of −1.91 cm/yr and the highest depletion of −10.98 cm/yr, similar to TWS, which agreed with the researcher’s findings [33]. Conversely, the southern region had the lowest value of 1.23 cm/yr and the greatest value of 3.97 cm/yr in the ΔGWS_GSFC data, while the ΔGWS_JPL data had the lowest value of 0.45 cm/yr and the maximum value of 8.28 cm/yr.
Throughout the years after 2018, GWS in the southern region has been significantly depleted. According to the ΔGWS_JPL data, the highest consumption is at −6.17 cm/yr and the lowest at −3.16 cm/yr. However, ΔGWS_GSFC data showed a maximum consumption at −5.82 cm/yr and lowest at −0.8 cm/yr.
Remote sensing data for 2022–2021 indicated a sharp decrease in GWS all over Iraq. ΔGWS_JPL recorded the largest and lowest GWS consumption values at −18.64 cm/yr and −3.16 cm/yr, respectively. In comparison, ΔGWS_GSFC recorded similar values at −20.03 cm/yr and −5.25 cm/yr, respectively, and coincides with the researcher’s findings [88].
Figure 4 shows the distribution of spatiotemporal variation in the ΔGWS trend in Iraq throughout this study. The recorded consumption values in ΔGWS ranged from −1.14 to −1.36 cm/yr for the GSFC and JPL, respectively; this can be analyzed as a result of excessive pumping of GW in the northern region, consistent with the researcher’s findings [32]. Inversely, in the southern region, the lowest consumption of ΔGWS ranged from 0.94 to 1.64 cm/yr for the GSFC and JPL, respectively.
In the analysis of GPM- and GPCP-derived rainfall data from 2002 to 2023, shown in Figure 5, the average annual precipitation was 400 mm/yr throughout the research period.
In addition, the average yearly accumulated rainfall over the preceding period (2003 to 2006) was higher than 500 mm/yr. Significant changes occurred in 2007–2009 due to the commencement of a drought and a rise in temperatures in the region [89]. A decline in the groundwater level was reflected in GRACE satellite data. Also, the results revealed a significant decline in SMS from 2006 to the end of 2008, as the decrease reached −5 cm/yr—primarily due to soil moisture fluctuations caused by climate change, which notably decreased precipitation. Thus, there is a decline in groundwater storage. From 2009 to the end of 2017, rainfall rates in the study area fluctuated significantly, ranging from 250 mm to 450 mm yearly. On the other hand, the period between 2005 and 2016 was shown by differences in the peaks and declines of ΔGWS_JPL, ΔGWS_GSFC, and ΔGWS_CSR as a result of fluctuations in rainfall. Although there was a significant increase in soil moisture between 2021 and the end of 2023, data revealed a substantial decrease in groundwater levels.
By conducting Pearson correlation analyses of annual ΔGWS and annual mean rainfall for the entire study period in Iraq, ΔGWS was well-correlated with rainfall with R ranging between 0.70 and 0.78. It is crucial to consider the significant p-value of ΔGWS in contrast to rainfall, which was less than 0.01 throughout the study period, as shown in Table 2.

3.2. Validity Evaluation

To ensure the reliability of the findings, statistical tests are required to compare the mascon JPL, GSFC, and CSR performance, as shown in Table 3. The GSFC mascon model showed superior performance in estimating ΔGWS in Iraq, achieving the lowest RMSE value of 0.28 mm and the highest R2 at 0.92, as shown in Figure 6, which shows a closed distribution of most points around the best fitting line (red dashed line), indicating a robust linear relationship; most of the residual values are positive and small. Thus, the strength of this relationship is enhanced with the following values: perfect NSE of 0.93 and R of 0.96. At the same time, RMSE is at 0.60 mm, R2 at 0.89, NSE at 0.89, and R at 0.94 for the JPL-mascon. Meanwhile, RMSE is at 0.53 mm, R2 at 0.82, NSE at 0.76, and R at 0.91 for the CSR-mascon. The GSFC performs better if the resulting NSE is greater than 0.75 and PBIAS is less than ±10. These results indicate that this mascon provides more accurate estimates of ΔGWS. On the other hand, the M-K trend test showed a declining trend for the ΔGWS and ΔSMS of 0.75 and 0.04, respectively, with Sen’s slope of 0.13 and 0.08, respectively.
Table 4 presents the ΔGWS’s degree of association as measured by various correlation coefficients: Pearson, Spearman, and Kendall. A positive perfect association is indicated by a correlation value of 1, a negative perfect association by a correlation value of −1, and no association is indicated by a correlation value of 0. The Pearson, Spearman, and Kendall correlation coefficient values do not significantly differ. There is no discernible deviation from linearity, indicating that the character of the association across the variables is fundamentally linear. The p-values are near zero (much less than 0.05), indicating that the connections are highly significant. For GSFC, all correlation coefficient values are extremely near 1; thus, this improved model expressed GWS.
To delineate and evaluate the distribution and difference in the computed depletion of groundwater, the average GWS_JPL’s spatiotemporal variation was classified into four classes (≤2, 2–4, 4–6, and ≥6), as shown in Figure 7 and Table 5. These classes represent the ratio of groundwater depletion in Iraq between 2002 and 2023.
This delineation indicated that classes 2–4 are the most common, occupying 36% and increasing to 43% of the study area, equivalent to 158,053 km2 and 189,809 km2 for 2002 and 2023, respectively. In contrast, class ≥ 6 is the least common, occupying only 5% (22,438 km2) of the study area, representing the minimal level of groundwater consumption in 2023. While 2002 occupied only 21% (91,592 km2) of the study area, this entails increasing the water consumption areas. Classes 4–6 are the least common, occupying only 12% (51,477 km2) in 2002. However, this increased in the year 2023 to 21% (90,375 km2) of the study area.
Class ≤ 2 was distributed over 31% (135,950 km2) and 31% (134,450 km2) for 2002 and 2023, respectively, representing the higher groundwater consumption level for the GWS_JPL.
Class ≥ 6 declined greatly from 21% (91,592 km2) in 2002 to 5% (22,438 km2) in 2023, indicating clear groundwater depletion in these regions.
For the GWS_GSFC, the spatial distribution of groundwater depletion varied greatly during 2002 and 2023; the average GWS_GSFC’s spatiotemporal variation was delineated by classifying the depletion ratio into five classes (≤1.6, 1.6–4, 4–7, 7–10, and ≥10), as shown in Figure 8 and Table 6. These classes represent the GWS_GSFC-based ratio of groundwater depletion in Iraq between 2002 and 2023.
This delineation shows that class ≤ 1.6 is the most common, occupying 40% and 38% of the study area, equivalent to 174,306 km2 and 171,098 km2 for 2002 and 2023, respectively, which represents a higher level of groundwater consumption for the GWS_GSFC. In contrast, class ≥ 10 is the least common, occupying only 5% and 1% of the study area, equivalent to 24,224 km2 and 1553 km2 for the years 2002 and 2023, respectively, which represents the minimal level of groundwater consumption; this entails increasing the areas of water consumption.
Classes 1.6–4 occupied only 6% of the area of (27,226 km2) in 2002, but in 2023, it climbed to 30% (129,791 km2) of the study area.
Classes 4–7 occupied only 35% (151,143 km2) in 2002, but it declined to 29% (127,436 km2) in 2023. However, classes 7–10 extended over only 14% (60,173 km2) in 2002 and declined significantly in 2023 to 2% (7194 km2) of the study area.
Classes 4–7, 7–10, and ≥10 declined greatly in 2023 from the levels of 2002, indicating a clear depletion of groundwater in these regions.

4. Uncertainty Analysis

The uncertainty analysis was performed on the ΔGWS derived from GRACE from TWS after subtracting SMS. The errors in GRACE-based GWS were obtained from TWS and SMS. Utilizing the law of error propagation, all errors were taken into account while estimating the uncertainties of GWS [54]. The uncertainty in TWS was calculated using the standard deviation of three mascon. Considering that TWS and non-GW component errors are separate and rarely have an impact on one another, the error of GRACE-GWS is the square root of the total of individual errors [33]. The uncertainty in ΔGWS_JPL, ΔGWS_GSFC, and ΔGWS_CSR are 5.11 cm/yr, 3.30 cm/yr, and 4.80 cm/yr, respectively.
In this paper, no further post-processing was performed on either the GRACE data or the data from the other hydrological variables, since the main focus of this research was to evaluate the implementation of GRACE at the aquifer scale.
Comprehensive analysis of temporal and spatial uncertainty requires the use of advanced methods and techniques, such as modern programming methods, which could be considered as a limitation of this study.
In future research, in situ well data will be integrated. Various mathematical methods are applied to handle data variation from the GRACE mascon (JPL, GSFC, and CSR) to handle Earth’s gravity data.
Certain studies utilized the analogous methods of processing employed for GRACE, i.e., spherical harmonics truncated at degree 60, a decorrelation filter for de-striping, and a 200 km Gaussian filter for smoothing, on other hydrologic elements [46,90,91]. Other studies integrated a priori knowledge regarding point mass alterations both inside and outside a basin that may induce leakage and affect the GRACE findings [71,92]. Uncertainty may not be uniform around the predicted values; it may vary, slightly larger or smaller, since regions with dense vegetation or intricate geology may be more prone to hydrological separation mistakes, which may bias the uncertainty.

5. Conclusions

This paper highlights the importance of utilizing satellites such as GRACE and simulations such as the GLDAS model, NOAH, GPM, and GPCP to assess long-term and short-term water storage, and these tools are handy in areas that lack extensive networks of monitoring stations. TWS dynamics are a powerful tool for improving estimates of change in GWS, as this can be achieved by subtracting all contributions of other hydrological variables from the GRACE signal. Analysis of GRACE data provides evidence of geographic variations in ΔGWS depletion rates between northern and southern Iraq from 2002 to 2023, with the north region experiencing more severe total water depletion than the south region, which maintained a lower water deficit.
The results demonstrated that during this study’s period, the principal components analysis of GRACE—TWS (SM, CWS, SWE, SWS, and GW) showed that SM and GW are the two components most influencing the GRACE—TWS. Meanwhile, other components, such as CWS, SWE, and SWS did not show significant effects, as their mean values were close to zero. Therefore, it was ignored.
The period from 2006 to the end of 2008 noticed a simultaneous decline in soil moisture groundwater storage. Although there was a significant increase in soil moisture between 2021 and the end of 2023, data revealed a substantial decrease in groundwater levels. This apparent contradiction is attributed to increased groundwater extraction rates, which has led to the depletion of these vital resources.
The GSFC mascon model showed superior performance in estimating ΔGWS from the JPL mascon model in Iraq. This study is the first to employ gravity data to close the information gap on short-term trends in groundwater storage in Iraq. It will help us better comprehend the country’s intricate hydrological dynamics.
In 2023, the results of the classification classes for the (GWS_JPL and GWS_GSFC) reflect a worrying trend of groundwater depletion and increased demand for near-surface resources. For the GWS_JPL, where Class ≥ 6 declined greatly from 21% (91,592 km2) in 2002 to 5% (22,438 km2) in 2023. Classes 2–4, which refer to a state near groundwater depletion, were the highest in 2023 at 43%, covering (189,809 km2) of the study’s area after previously being 36% (158,053 km2), in 2002. While, for GWS_GSFC, classes 4–7, 7–10, and ≥10 declined greatly in 2023 from the levels of 2002, indicating a clear depletion of groundwater in these regions.
Remote sensing technology such as GRACE satellite plays an essential role in monitoring GW by providing information about the groundwater level and changes in groundwater. This makes predicting and showing these changes easier. On the other hand, these images have numerous benefits, including data that can be collected at any time of day and any weather condition, the ability to cover large areas with precision, and it being difficult to access, especially if field data or financial resources are lacking.

6. Recommendations

Considering the water scarcity Iraq encounters, the rapid depletion of groundwater aquifers due to intensive groundwater extraction from the reduction in surface water resources. The necessity for the formulation of cohesive scientific plans for water resource management is emphasized. An important step is the digging of additional shallow and deep groundwater wells, integrating this with GRACE gravity satellite data to regionally monitor groundwater levels and establish a complete groundwater database.
This will aid in the conceptualization of aquifer systems and the computation of their hydrologic properties, as well as the assessment of annual groundwater recharge levels necessary for improving groundwater storage.
Integrating the openly accessible data generated by GRACE and GLDAS, together with additional satellite systems managed by NASA and other space organizations, can enhance and facilitate the assessment of many facets of water resources and include them with the local network to enhance our comprehension of water resource variety and offer significant insights into the management and conservation of water resources in Iraq.
The uncertainty analysis of ΔGWS obtained from GRACE data is crucial for ensuring the reliability of the estimations. The disparities across the mascon (JPL, GSFC, and CSR) illustrate the data’s sensitivity to the employed approaches. This necessitates the application of integrated methodologies encompassing hydrological models, in situ measurements (monitoring well data), and temporal and spatial variation analysis of uncertainty.

Author Contributions

Conceptualization, M.S.A. and I.A.A.; methodology, I.A.A. and M.S.A.; software, H.K.M.; validation, I.A.A., M.S.A. and H.K.M.; formal analysis, I.A.A., M.S.A. and H.K.M.; investigation, M.S.A., I.A.A. and H.K.M.; resources, H.K.M.; data curation, I.A.A., M.S.A. and H.K.M.; writing—original draft preparation, M.S.A., I.A.A. and H.K.M.; writing—review and editing, I.A.A., M.S.A. and H.K.M.; visualization, M.S.A. and I.A.A.; supervision, I.A.A. and M.S.A.; project administration, I.A.A., M.S.A. and H.K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data are publicly available on the website NASA.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Economic Forum Annual Meeting 2011. Shared Norms for the New Reality, Davos-Klosters, Switzerland, 26–30 January 2011. Available online: https://www.weforum.org (accessed on 18 December 2024).
  2. Sahoo, S.; Swain, S.; Goswami, A.; Sharma, R.; Pateriya, B. Assessment of Trends and Multi-Decadal Changes in Groundwater Level in Parts of the Malwa Region, Punjab, India. Groundw. Sustain. Dev. 2021, 14, 100644. [Google Scholar] [CrossRef]
  3. AlJumaili, K.K.; Al-Khafaji, M.S.; Al-Awadi, A.T. Assessment of Evapotranspiration Estimation Models for Irrigation Projects in Karbala. Iraq. Eng. Technol. J. 2014, 32, 1149–1157. [Google Scholar] [CrossRef]
  4. Keulertz, M.; Mohtar, R. Water-Energy-Food Nexus in Libya, UAE, Egypt and Iraq. JSTOR 2022. Available online: https://www.jstor.org/stable/resrep39813 (accessed on 28 February 2025).
  5. Sayl, K.N.; Muhammad, N.S.; El-Shafie, A. Robust Approach for Optimal Positioning and Ranking Potential Rainwater Harvesting Structure (RWH): A Case Study of Iraq. Arab. J. Geosci. 2017, 10, 413. [Google Scholar] [CrossRef]
  6. Kandeel, A. Climate Change: The Middle East Faces a Water Crisis. Available online: http://www.mei.edu/content/article/climate-change-middle-east-faces-water-crisis (accessed on 2 January 2025).
  7. Muhaisen, N.; Khayyun, T.; Al-Mukhtar, M. Drought Forecasting Model for Future Climate Change Effects in a Regional Catchment Area in Northern Iraq. Eng. Technol. J. 2024, 42, 1–15. [Google Scholar] [CrossRef]
  8. Saeed, F.H.; Al-Khafaji, M.S.; Al-Faraj, F. Hydrologic Response of Arid and Semi-Arid River Basins in Iraq under a Changing Climate. J. Water Clim. Change 2022, 13, 1225–1240. [Google Scholar] [CrossRef]
  9. Saeed, F.H.; Al-Khafaji, M.S.; Al-Faraj, F.A.M.; Uzomah, V. Sustainable Adaptation Plan in Response to Climate Change and Population Growth in the Iraqi Part of Tigris River Basin. Sustainability 2024, 16, 2676. [Google Scholar] [CrossRef]
  10. Alwan, I.A.; Aziz, N.A.; Hamoodi, M.N. Potential Water Harvesting Sites Identification Using Spatial Multi-Criteria Evaluation in Maysan Province, Iraq. ISPRS Int. J. Geo-Inf. 2020, 9, 235. [Google Scholar] [CrossRef]
  11. Al-Gburi, M.R.G.; Al-Khatony, S.E.; Znad, R.K.; Al-Sumaidaie, M.A.H. Mapping of Groundwater Potential Zone Using GIS and Remote Sensing of Shwan Sub-Basin, Kirkuk, NE Iraq. Iraqi Geol. J. 2022, 55, 62–72. [Google Scholar] [CrossRef]
  12. Ethaib, S.; Zubaidi, S.L.; Al-Ansari, N. Evaluation Water Scarcity Based on GIS Estimation and Climate-Change Effects: A Case Study of Thi-Qar Governorate, Iraq. Cogent Eng. 2022, 9, 2075301. [Google Scholar] [CrossRef]
  13. Tarish, H.A.; Mahdi, M.S.; Croock, M.S. Wireless Sensor Network Structure for Ground Water Well’s Field in Karbala City. Int. J. Comput. Appl. 2017, 975, 8887. [Google Scholar]
  14. Gleick, P.H.; Howe, C.W. Water in Crisis: A Guide to the World’s Fresh Water Resources. Clim. Change 1995, 31, 119–122. [Google Scholar]
  15. Margat, J.; Van der Gun, J. Groundwater around the World: A Geographic Synopsis; CRC Press: Boca Raton, FL, USA, 2013; ISBN 1138000345. [Google Scholar]
  16. Ramjeawon, M.; Demlie, M.; Toucher, M. Analyses of Groundwater Storage Change Using GRACE Satellite Data in the Usutu-Mhlatuze Drainage Region, North-Eastern South Africa. J. Hydrol. Reg. Stud. 2022, 42, 101118. [Google Scholar] [CrossRef]
  17. Liu, L.; Chen, X.; Xu, G.; Shu, L. Use of Hydrologic Time-Series Data for Identification of Hydrodynamic Function and Behavior in a Karstic Water System in China. Hydrogeol. J. 2011, 8, 1577–1585. [Google Scholar] [CrossRef]
  18. Aziz, N.A.; Hasan, R.H.; Abdulrazzaq, Z.T. Optimum Site Selection for Groundwater Wells Using Integration between GIS and Hydrogeophysical Data. Eng. Technol. J. 2018, 36, 596–602. [Google Scholar] [CrossRef]
  19. Khalaf, A.G. Determination the suiTable locations for drilling wells for irrigation purpose by using geographic information system (GIS). Eng. Technol. J. 2016, 34, 80–89. [Google Scholar] [CrossRef]
  20. Aziz, N.A.; Alwan, I.A. An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al-Hawizeh Marsh Area, Southern Iraq. Geomat. Environ. Eng. 2021, 15, 5–21. [Google Scholar] [CrossRef]
  21. Alwan, I.A.; Karim, H.H.; Aziz, N.A. Investigate the Optimum Agricultural Crops Production Seasons in Salah Al-Din Governorate Utilizing Climate Remote Sensing Data and Agro-Climatic Zoning. Iraqi J. Sci. 2019, 60, 2087–2094. [Google Scholar] [CrossRef]
  22. Huang, C.; Chen, Y.; Zhang, S.; Wu, J. Detecting, Extracting, and Monitoring Surface Water from Space Using Optical Sensors: A Review. Rev. Geophys. 2018, 56, 333–360. [Google Scholar] [CrossRef]
  23. Cui, L.; Song, Z.; Luo, Z.; Zhong, B.; Wang, X.; Zou, Z. Comparison of Terrestrial Water Storage Changes Derived from GRACE/GRACE-FO and Swarm: A Case Study in the Amazon River Basin. Water 2020, 12, 3128. [Google Scholar] [CrossRef]
  24. Chen, W.; Xiong, Y.; Zhong, M.; Yang, Z.; Shum, C.K.; Li, W.; Liang, L.; Li, Q. Twenty-Year Spatiotemporal Variations of TWS over Mainland China Observed by GRACE and GRACE Follow-On Satellites. Atmosphere 2023, 14, 1717. [Google Scholar] [CrossRef]
  25. Masood, A.; Atiq, M.; Rahman, U.; Zia, M.; Rahman, U.; Waseem, M.; Sarwar, M.K.; Ali, W.; Farooq, R.; Almazroui, M.; et al. An Overview of Groundwater Monitoring through Point-To. Water 2022, 14, 565. [Google Scholar] [CrossRef]
  26. Alattar, M.H.; Troy, T.J.; Russo, T.A.; Boyce, S.E. Modeling the Surface Water and Groundwater Budgets of the US Using MODFLOW-OWHM. Adv. Water Resour. 2020, 143, 103682. [Google Scholar] [CrossRef]
  27. Binley, A.; Hubbard, S.S.; Huisman, J.A.; Revil, A.; Robinson, D.A.; Singha, K.; Slater, L.D. The Emergence of Hydrogeophysics for Improved Understanding of Subsurface Processes over Multiple Scales. Water Resour. Res. 2015, 51, 3837–3866. [Google Scholar] [CrossRef] [PubMed]
  28. Barthel, R.; Banzhaf, S. Groundwater and Surface Water Interaction at the Regional-Scale–a Review with Focus on Regional Integrated Models. Water Resour. Manag. 2016, 30, 1–32. [Google Scholar] [CrossRef]
  29. Ajami, H.; McCabe, M.F.; Evans, J.P.; Stisen, S. Assessing the Impact of Model Spin-up on Surface Water-groundwater Interactions Using an Integrated Hydrologic Model. Water Resour. Res. 2014, 50, 2636–2656. [Google Scholar] [CrossRef]
  30. Kumar, K.S.; Sridhar, V.; Varaprasad, B.J.S.; Chinnapa Reddy, K. Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India. Water 2022, 14, 3852. [Google Scholar] [CrossRef]
  31. Sarkar, T.; Kannaujiya, S.; Taloor, A.K.; Champati Ray, P.K.; Chauhan, P. Integrated Study of GRACE Data Derived Interannual Groundwater Storage Variability over Water Stressed Indian Regions. Groundw. Sustain. Dev. 2020, 10, 100376. [Google Scholar] [CrossRef]
  32. Othman, A.; Abdelrady, A.; Mohamed, A. Monitoring Mass Variations in Iraq Using Time-Variable Gravity Data. Remote Sens. 2022, 14, 3346. [Google Scholar] [CrossRef]
  33. Voss, K.A.; Famiglietti, J.S.; Lo, M.; De Linage, C.; Rodell, M.; Swenson, S.C. Groundwater Depletion in the Middle East from GRACE with Implications for Transboundary Water Management in the Tigris-Euphrates-Western Iran Region. Water Resour. Res. 2013, 49, 904–914. [Google Scholar] [CrossRef]
  34. Mohamed, A.; Al Deep, M. Depth to the Bottom of the Magnetic Layer, Crustal Thickness, and Heat Flow in Africa: Inferences from Gravity and Magnetic Data. J. Afr. Earth Sci. 2021, 179, 104204. [Google Scholar] [CrossRef]
  35. Mohamed, A.; El Ella, E.M.A. Magnetic Applications to Subsurface and Groundwater Investigations: A Case Study from Wadi El Assiuti, Egypt. Int. J. Geosci. 2021, 12, 77. [Google Scholar] [CrossRef]
  36. Othman, A. Measuring and Monitoring Land Subsidence and Earth Fissures in Al-Qassim Region, Saudi Arabia: Inferences from InSAR. In Proceedings of the Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Hammamet, Tunisia, 12–18 November 2018; Springer: Berlin/Heidelberg, Germany, 2019; pp. 287–291. [Google Scholar]
  37. Alattar, M.H. Mapping Groundwater Dynamics in Iraq: Integrating Multi-Data Sources for Comprehensive Analysis. Model. Earth Syst. Environ. 2024, 10, 4375–4385. [Google Scholar] [CrossRef]
  38. Al-Ansari, N.; Saleh, S.; Abdullahand, T.; Ali Abed, S. Quality of Surface Water and Groundwater in Iraq. J. Earth Sci. Geotech. Eng. 2020, 11, 161–199. [Google Scholar] [CrossRef] [PubMed]
  39. Adamo, N.; Al-Ansari, N.; Sissakian, V.; Fahmi, K.J.; Abed, S.A. Climate Change: Droughts and Increasing Desertification in the Middle East, with Special Reference to Iraq. Engineering 2022, 14, 235–273. [Google Scholar] [CrossRef]
  40. Ali, E.H.; Bulqader, M.A.; Baker, Y.T. Measuring The Impact of Water Scarcity on Agricultural Economic Development in Iraq. Stallion J. Multidiscip. Assoc. Res. Stud. 2024, 3, 1–16. [Google Scholar] [CrossRef]
  41. Al-Ansari, N.; Adamo, N.; Hachem, A.H.; Sissakian, V.; Laue, J.; Abed, S.A. Causes of Water Resources Scarcity in Iraq and Possible Solutions. Engineering 2023, 15, 467–496. [Google Scholar] [CrossRef]
  42. Al-Ansari, N. Topography and Climate of Iraq. J. Earth Sci. Geotech. Eng. 2021, 11, 1–13. [Google Scholar] [CrossRef]
  43. Frenken, K. Irrigation in the Middle East Region in Figures AQUASTAT Survey-2008; Water Reports; FAO: Roma, Italy, 2009. [Google Scholar]
  44. Al-Jiburi, H.K.; Al-Basrawi, N.H. Hydrogeological Map of Iraq, Scale 1: 1000 000, 2013. Iraqi Bull. Geol. Min. 2015, 11, 17–26. [Google Scholar]
  45. Yousuf, M.A.; Rapantova, N.; Younis, J.H. Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility. Water 2018, 10, 1651. [Google Scholar] [CrossRef]
  46. Longuevergne, L.; Scanlon, B.R.; Wilson, C.R. GRACE Hydrological Estimates for Small Basins: Evaluating Processing Approaches on the High Plains Aquifer, USA. Water Resour. Res. 2010, 46, W11517. [Google Scholar] [CrossRef]
  47. Morris, B.L.; Lawrence, A.R.L.; Chilton, P.J.C.; Adams, B.; Calow, R.C.; Klinck, B.A. Groundwater and Its Susceptibility to Degradation: A Global Assessment of the Problem and Options for Management. 2003. Available online: https://nora.nerc.ac.uk/id/eprint/19395 (accessed on 28 February 2025).
  48. Alsdorf, D.E.; Rodríguez, E.; Lettenmaier, D.P. Measuring Surface Water from Space. Rev. Geophys. 2007, 45. [Google Scholar] [CrossRef]
  49. Rast, M.; Johannessen, J.; Mauser, W. Review of Understanding of Earth’s Hydrological Cycle: Observations, Theory and Modelling. Surv. Geophys. 2014, 35, 491–513. [Google Scholar] [CrossRef]
  50. Huang, J. How the Regional Water Cycle Responds to Recent Climate Change in Northwest Aridzone of China? Doctoral Thesis, Hong Kong Baptist University, Hong Kong, 2017. [Google Scholar]
  51. Tapley, B.D.; Bettadpur, S.; Ries, J.C.; Thompson, P.F.; Watkins, M.M. GRACE Measurements of Mass Variability in the Earth System. Science 2004, 305, 503–505. [Google Scholar] [CrossRef]
  52. Feng, W.; Shum, C.K.; Zhong, M.; Pan, Y. Groundwater Storage Changes in China from Satellite Gravity: An Overview. Remote Sens. 2018, 10, 674. [Google Scholar] [CrossRef]
  53. Frappart, F.; Ramillien, G. Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review. Remote Sens. 2018, 10, 829. [Google Scholar] [CrossRef]
  54. Rodell, M.; Houser, P.R.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M.; et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef]
  55. Richey, A.S.; Thomas, B.F.; Lo, M.; Reager, J.T.; Famiglietti, J.S.; Voss, K.; Swenson, S.; Rodell, M. Quantifying Renewable Groundwater Stress with GRACE. Water Resour. Res. 2015, 51, 5217–5238. [Google Scholar] [CrossRef]
  56. Chao, N.; Luo, Z.; Wang, Z.; Jin, T. Retrieving Groundwater Depletion and Drought in the Tigris-Euphrates Basin Between 2003 and 2015. Groundwater 2018, 56, 770–782. [Google Scholar] [CrossRef]
  57. Taylor, R.G.; Scanlon, B.; Döll, P.; Rodell, M.; van Beek, R.; Wada, Y.; Longuevergne, L.; Leblanc, M.; Famiglietti, J.S.; Edmunds, M.; et al. Ground Water and Climate Change. Nat. Clim. Change 2013, 3, 322–329. [Google Scholar] [CrossRef]
  58. Joodaki, G.; Wahr, J.; Swenson, S. Estimating the Human Contribution to Groundwater Depletion in the Middle East, from GRACE Data, Land Surface Models, and Well Observations. Water Resour. Res. 2014, 50, 2679–2692. [Google Scholar] [CrossRef]
  59. Zens, J.; Flechtner, F.; Wickert, J.; Förste, C.; Stolle, C.; Grunwaldt, L.; Leicht, J.; Friedrich, D.; Itzerott, S. Nützliche Neue Begleiter Am Himmel: Satelliten Als Erdbeobachtungsinstrumente Haben Eine Lange Tradition Auf Dem Potsdamer Telegrafenberg. Syst. Erde 2017, 7, 6–11. [Google Scholar]
  60. Kornfeld, R.P.; Arnold, B.W.; Gross, M.A.; Dahya, N.T.; Klipstein, W.M.; Gath, P.F.; Bettadpur, S. GRACE-FO: The Gravity Recovery and Climate Experiment Follow-On Mission. J. Spacecr. Rockets 2019, 56, 931–951. [Google Scholar] [CrossRef]
  61. Wahr, J.; Swenson, S.; Zlotnicki, V.; Velicogna, I. Time-variable Gravity from GRACE: First Results. Geophys. Res. Lett. 2004, 31. [Google Scholar] [CrossRef]
  62. Moghim, S. Assessment of Water Storage Changes Using GRACE and GLDAS. Water Resour. Manag. 2020, 34, 685–697. [Google Scholar] [CrossRef]
  63. McStraw, T.C.; Pulla, S.T.; Jones, N.L.; Williams, G.P.; David, C.H.; Nelson, J.E.; Ames, D.P. An Open-Source Web Application for Regional Analysis of GRACE Groundwater Data and Engaging Stakeholders in Groundwater Management. JAWRA J. Am. Water Resour. Assoc. 2022, 58, 1002–1016. [Google Scholar] [CrossRef]
  64. Long, D.; Longuevergne, L.; Scanlon, B.R. Global Analysis of Approaches for Deriving Total Water Storage Changes from GRACE Satellites. Water Resour. Res. 2015, 51, 2574–2594. [Google Scholar] [CrossRef]
  65. Rahaman, M.M.; Thakur, B.; Kalra, A.; Ahmad, S. Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology 2019, 6, 19. [Google Scholar] [CrossRef]
  66. Bonan, G.B.; Oleson, K.W.; Vertenstein, M.; Levis, S.; Zeng, X.; Dai, Y.; Dickinson, R.E.; Yang, Z.-L. The Land Surface Climatology of the Community Land Model Coupled to the NCAR Community Climate Model. J. Clim. 2002, 15, 3123–3149. [Google Scholar] [CrossRef]
  67. Deng, H.; Pepin, N.C.; Liu, Q.; Chen, Y. Understanding the Spatial Differences in Terrestrial Water Storage Variations in the Tibetan Plateau from 2002 to 2016. Clim. Change 2018, 151, 379–393. [Google Scholar] [CrossRef]
  68. Liu, F.; Kang, P.; Zhu, H.; Han, J.; Huang, Y. Analysis of Spatiotemporal Groundwater-Storage Variations in China from GRACE. Water 2021, 13, 2378. [Google Scholar] [CrossRef]
  69. Scanlon, B.R.; Longuevergne, L.; Long, D. Ground Referencing GRACE Satellite Estimates of Groundwater Storage Changes in the California Central Valley, USA. Water Resour. Res. 2012, 48. [Google Scholar] [CrossRef]
  70. Massoud, E.C.; Purdy, A.J.; Miro, M.E.; Famiglietti, J.S. Projecting Groundwater Storage Changes in California’s Central Valley. Sci. Rep. 2018, 8, 12917. [Google Scholar] [CrossRef] [PubMed]
  71. Landerer, F.W.; Swenson, S.C. Accuracy of Scaled GRACE Terrestrial Water Storage Estimates. Water Resour. Res. 2012, 48. [Google Scholar] [CrossRef]
  72. Cooley, S.S.; Landerer, F.W. Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-3 Data Product User Handbook; Jet Propulsion Laboratory California Institute of Technology: La Cañada Flintridge, CA, USA, 2019. [Google Scholar]
  73. Mohamed, A. Gravity Applications in Estimating the Mass Variations in the Middle East: A Case Study from Iran. Arab. J. Geosci. 2020, 13, 364. [Google Scholar] [CrossRef]
  74. Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
  75. Mann, H.B. Nonparametric Tests against Trend. Econom. J. Econom. Soc. 1945, 13, 245–259. [Google Scholar] [CrossRef]
  76. Fieller, E.C.; Hartley, H.O.; Pearson, E.S. Tests for Rank Correlation Coefficients. I. Biometrika 1957, 44, 470–481. [Google Scholar] [CrossRef]
  77. Ndlovu, M.S.; Demlie, M. Assessment of Meteorological Drought and Wet Conditions Using Two Drought Indices across KwaZulu-Natal Province, South Africa. Atmosphere 2020, 11, 623. [Google Scholar] [CrossRef]
  78. Patle, G.T.; Singh, D.K.; Sarangi, A.; Rai, A.; Khanna, M.; Sahoo, R.N. Time Series Analysis of Groundwater Levels and Projection of Future Trend. J. Geol. Soc. India 2015, 85, 232–242. [Google Scholar] [CrossRef]
  79. Sun, A.Y. Predicting Groundwater Level Changes Using GRACE Data. Water Resour. Res. 2013, 49, 5900–5912. [Google Scholar] [CrossRef]
  80. Chai, T.; Draxler, R.R. Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?—Arguments against Avoiding RMSE in the Literature. Geosci. Model Dev. 2014, 7, 1247–1250. [Google Scholar] [CrossRef]
  81. Zhang, X.-J.; Tang, Q.; Pan, M.; Tang, Y. A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. J. Hydrometeorol. 2014, 15, 2067–2084. [Google Scholar] [CrossRef]
  82. Gupta, H.V.; Sorooshian, S.; Yapo, P.O. Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration. J. Hydrol. Eng. 1999, 4, 135–143. [Google Scholar] [CrossRef]
  83. Abou Rafee, S.A.; Uvo, C.B.; Martins, J.A.; Domingues, L.M.; Rudke, A.P.; Fujita, T.; Freitas, E.D. Large-Scale Hydrological Modelling of the Upper Paraná River Basin. Water 2019, 11, 882. [Google Scholar] [CrossRef]
  84. Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
  85. Skaskevych, A. A Comparison Study of Grace-Based Groundwater Modeling for Data-Rich and Data-Poor Regions; University of Missouri-Kansas City: Kansas City, MO, USA, 2014; ISBN 1321264976. [Google Scholar]
  86. Seraphin, P.; Gonçalvès, J.; Hamelin, B.; Stieglitz, T.; Deschamps, P. Influence of Intensive Agriculture and Geological Heterogeneity on the Recharge of an Arid Aquifer System (Saq–Ram, Arabian Peninsula) Inferred from GRACE Data. Hydrol. Earth Syst. Sci. 2022, 26, 5757–5771. [Google Scholar] [CrossRef]
  87. Al-Mohammdawi, J.A.Z. A Study of Terrestrial Water Storage of Iraq Using GRACE Satellite Data and GLDAS System. Ph.D. Thesis, College of Science, University of Basrah, Basra, Iraq, 2023, unpublished. 168p. [Google Scholar]
  88. Nikraftar, Z.; Parizi, E.; Saber, M.; Hosseini, S.M.; Ataie-Ashtiani, B.; Simmons, C.T. Groundwater Sustainability Assessment in the Middle East Using GRACE/GRACE-FO Data. Hydrogeol. J. 2024, 32, 321–337. [Google Scholar] [CrossRef]
  89. Mulder, G.; Olsthoorn, T.N.; Al-Manmi, D.A.M.A.; Schrama, E.J.O.; Smidt, E.H. Identifying Water Mass Depletion in Northern Iraq Observed by GRACE. Hydrol. Earth Syst. Sci. 2015, 19, 1487–1500. [Google Scholar] [CrossRef]
  90. Nanteza, J.; de Linage, C.R.; Thomas, B.F.; Famiglietti, J.S. Monitoring Groundwater Storage Changes in Complex Basement Aquifers: An Evaluation of the GRACE Satellites over E Ast A Frica. Water Resour. Res. 2016, 52, 9542–9564. [Google Scholar] [CrossRef]
  91. Döll, P.; Müller Schmied, H.; Schuh, C.; Portmann, F.T.; Eicker, A. Global-scale Assessment of Groundwater Depletion and Related Groundwater Abstractions: Combining Hydrological Modeling with Information from Well Observations and GRACE Satellites. Water Resour. Res. 2014, 50, 5698–5720. [Google Scholar] [CrossRef]
  92. Longuevergne, L.; Wilson, C.R.; Scanlon, B.R.; Crétaux, J.F. GRACE Water Storage Estimates for the Middle East and Other Regions with Significant Reservoir and Lake Storage. Hydrol. Earth Syst. Sci. 2013, 17, 4817–4830. [Google Scholar] [CrossRef]
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Figure 1. Framework of methodology.
Figure 1. Framework of methodology.
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Figure 2. Spatial and temporal fluctuations of ΔGWS_GSFC during the period 2002 to 2023 across Iraq.
Figure 2. Spatial and temporal fluctuations of ΔGWS_GSFC during the period 2002 to 2023 across Iraq.
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Figure 3. Spatial and temporal fluctuations of ΔGWS_JPL during the period 2002 to 2023 across Iraq.
Figure 3. Spatial and temporal fluctuations of ΔGWS_JPL during the period 2002 to 2023 across Iraq.
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Figure 4. ΔGWS trend’s spatiotemporal distribution during the whole study period.
Figure 4. ΔGWS trend’s spatiotemporal distribution during the whole study period.
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Figure 5. Comparison of precipitation, ΔGWS, and ΔSMS throughout of the study area.
Figure 5. Comparison of precipitation, ΔGWS, and ΔSMS throughout of the study area.
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Figure 6. Determination coefficient R2 over Iraq throughout the study.
Figure 6. Determination coefficient R2 over Iraq throughout the study.
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Figure 7. Classify average GWS_JPL (a) 2002 and (b) 2023.
Figure 7. Classify average GWS_JPL (a) 2002 and (b) 2023.
Hydrology 12 00069 g007
Hydrology 12 00069 g008
Figure 8. Classify the GWS_GSFC (a) 2002 and (b) 2023.
Figure 8. Classify the GWS_GSFC (a) 2002 and (b) 2023.
Hydrology 12 00069 g008
Table 1. Description of the utilized data.
Table 1. Description of the utilized data.
VariablesData TypeTemporal Resolution Spatial ResolutionData Source
JPL MasconJPL-RL06.1v32002–2023
Monthly		0.5° × 0.5°
Resample 0.05° × 0.05°		https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3.
Available formats Net—CDF.
Accessed on 16 April 2024
GSFC Mascon GSFC-RL06v022002–2023 Monthly0.5° × 0.5°
Resample 0.05° × 0.05°		https://earth.gsfc.nasa.gov/geo/data/grace-mascons.
Available format Net—CDF.
Accessed on 16 April 2024
CSR
Mascon		CSR-RL06v032002–2023 Monthly1° × 1°https://www2.csr.utexas.edu/grace/RL06_mascons.html
Available format Net—CDF.
Accessed on 5 February 2025
SMSGLDAS model NOAH025_Mv2.12002–2023 Monthly0.25° × 0.25°
Resample 0.05° × 0.05°		https://giovanni.gsfc.nasa.gov/giovanni.
Available format Net—CDF.
Accessed on 16 April 2024
SWE, SWS, CWS.GLDAS model NOAH025_Mv2.12002–2023 Monthly0.25° × 0.25°
Resample 0.05° × 0.05°		https://giovanni.gsfc.nasa.gov/giovanni.
Available format Net—CDF.
Accessed on 16 April 2024
GPMGPM_3IMERGM v072002–2023 Monthly0.1° × 0.1°
GPCPGPCPMON v3.22002–2023 Monthly0.5° × 0.5°
Table 2. Pearson correlation analyses of annual ΔGWS and mean rainfall in the study area.
Table 2. Pearson correlation analyses of annual ΔGWS and mean rainfall in the study area.
ΔGWSrp-Value
JPL0.78<0.01
GSFC0.76<0.01
CSR0.70<0.01
Table 3. ΔGWS’s main statistics.
Table 3. ΔGWS’s main statistics.
ΔGWSJPLGSFCCSR
Parameter
RMSE (mm)0.600.280.53
NSE0.890.930.76
Correlation coefficient (R)0.940.960.91
Coefficient of determinate R20.890.920.82
PBIAS1.63−0.753.08
Table 4. Correlations of ΔGWS obtained from GRACE satellite data.
Table 4. Correlations of ΔGWS obtained from GRACE satellite data.
ParameterPearsonSpearmanKendall
ΔGWS Correlationp-ValueCorrelationp-ValueCorrelationp-Value
JPL0.941.1 × 10−100.941.3 × 10−100.803.91 × 10−7
GSFC0.962.1 × 10−90.961.5 × 10−80.851.83 × 10−6
CSR0.911.0 × 10−80.881.2 × 10−70.724.43 × 10−6
Table 5. GWS_JPL depletion ratio in Iraq.
Table 5. GWS_JPL depletion ratio in Iraq.
Class20022023
Ratio %Area (Km2)Ratio %Area (Km2)
≤231135,95031134,450
2–436158,05343189,809
4–61251,4772190,375
≥62191,592522,438
Table 6. GWS_GSFC depletion ratio in Iraq.
Table 6. GWS_GSFC depletion ratio in Iraq.
Class20022023
Ratio %Area (Km2)Ratio %Area (Km2)
≤1.640174,30638171,098
1.6–4627,22630129,791
4–735151,14329127,436
7–101460,17327194
≥10524,22411553
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Mohammed, H.K.; Alwan, I.A.; Al-Khafaji, M.S. Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data. Hydrology 2025, 12, 69. https://doi.org/10.3390/hydrology12040069

AMA Style

Mohammed HK, Alwan IA, Al-Khafaji MS. Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data. Hydrology. 2025; 12(4):69. https://doi.org/10.3390/hydrology12040069

Chicago/Turabian Style

Mohammed, Hanan Kaduim, Imzahim A. Alwan, and Mahmoud Saleh Al-Khafaji. 2025. "Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data" Hydrology 12, no. 4: 69. https://doi.org/10.3390/hydrology12040069

APA Style

Mohammed, H. K., Alwan, I. A., & Al-Khafaji, M. S. (2025). Comparative Analysis of Spatiotemporal Variability of Groundwater Storage in Iraq Using GRACE Satellite Data. Hydrology, 12(4), 69. https://doi.org/10.3390/hydrology12040069

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